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• W2997233412 abstract "Intrinsic graph convolution operators with differentiable kernel functions play a crucial role in analyzing 3D shape meshes. In this paper, we present a fast and efficient intrinsic mesh convolution operator that does not rely on the intricate design of kernel function. We explicitly formulate the order of aggregating neighboring vertices, instead of learning weights between nodes, and then a fully connected layer follows to fuse local geometric structure information with vertex features. We provide extensive evidence showing that models based on this convolution operator are easier to train, and can efficiently learn invariant shape features. Specifically, we evaluate our method on three different types of tasks of dense shape correspondence, 3D facial expression classification, and 3D shape reconstruction, and show that it significantly outperforms state-of-the-art approaches while being significantly faster, without relying on shape descriptors." @default.
• W2997233412 created "2020-01-10" @default.
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• W2997233412 date "2019-10-01" @default.
• W2997233412 modified "2023-10-14" @default.
• W2997233412 title "SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator" @default.
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• W2997233412 doi "https://doi.org/10.1109/iccvw.2019.00509" @default.
• W2997233412 hasPublicationYear "2019" @default.
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